/modal

name: modal
description: Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
license: Apache-2.0
compatibility: Requires Python 3.10-3.14 and modal 1.6.0. Cloud execution needs a Modal account, authentication and network access; GPU use needs a payment method. Workload dependencies belong in their container Images.
metadata:
  version: "1.5"
  last-reviewed: "2026-09-30"
  tested-sdk: "modal 1.6.0"
  skill-author: K-Dense Inc.
  openclaw:
    envVars:
    - name: MODAL_TOKEN_ID
      required: false
      description: Modal token id.
    - name: MODAL_TOKEN_SECRET
      required: false
      description: Modal token secret.

Modal

Overview

Modal is a cloud platform for running Python code serverlessly, with a focus on AI/ML workloads. Key capabilities:

• **GPU compute** on demand (including H100, H200, B200 and B300)
• **Serverless functions** with autoscaling from zero to thousands of containers
• **Custom container images** built entirely in Python code
• **Persistent storage** via Volumes for model weights and datasets
• **Web endpoints** for serving models and APIs
• **Scheduled jobs** via cron or fixed intervals
• **Container reuse** and warm pools to reduce cold starts (model loading still takes time)

Modal Apps and Images are defined in Python; existing Dockerfiles are also supported.

When to Use This Skill

Use this skill when:

• Deploy or serve AI/ML models in the cloud
• Run GPU-accelerated computations (training, inference, fine-tuning)
• Create serverless web APIs or endpoints
• Scale batch processing jobs in parallel
• Schedule recurring tasks (data pipelines, retraining, scraping)
• Need persistent cloud storage for model weights or datasets
• Want to run code in custom container environments
• Build job queues or async task processing systems

Installation and Authentication

Install

uv pip install "modal==1.6.0"

Reviewed against SDK 1.6.0 and current [release notes](https://modal.com/docs/sdk/py/releases). Local SDK construction and selected handlers were tested; remote builds, deployments, GPU inference and cloud limits were not executed. Snippets using model/data placeholders or third-party workloads are illustrative and require their stated dependencies.

Authenticate

Prefer existing credentials before creating new ones. Only the two Modal-specific variables below are relevant — do not read, load, or expose any other environment variables or `.env` file contents:

1. Reuse an existing Modal profile, or check whether both `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` are set; report presence only. 2. If not, look up only those two keys in a local `.env` file (ignore all other entries) and load them if appropriate for the workflow. 3. Only fall back to interactive `modal setup` if no usable profile or token pair exists. The SDK supports profiles in `.modal.toml`; environment tokens are optional when a profile authenticates.

modal setup

This opens a browser for authentication. For CI/CD or headless environments, use environment variables:

export MODAL_TOKEN_ID=<your-token-id>
export MODAL_TOKEN_SECRET=<your-token-secret>

If neither an existing profile nor a token pair is available, create credentials at https://modal.com/settings

Check [current pricing](https://modal.com/pricing) and workspace quotas before sizing a run. GPU use requires a payment method even when credits remain.

**Reference**: See `references/getting-started.md` for detailed setup and first app walkthrough.

Core Concepts

App and Functions

A Modal `App` groups related functions. Functions decorated with `@app.function()` run remotely in the cloud:

import modal

app = modal.App("my-app")

@app.function()
def square(x):
    return x ** 2

@app.local_entrypoint()
def main():
    # .remote() runs in the cloud
    print(square.remote(42))

Run with `modal run script.py`. Deploy with `modal deploy script.py`.

**Reference**: See `references/functions.md` for lifecycle hooks, classes, `.map()`, `.spawn()`, and more.

Container Images

Modal builds container images from Python code. The recommended package installer is `uv`:

image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("torch==2.12.0", "transformers==5.9.0", "accelerate==1.13.0")
    .apt_install("git")
)

@app.function(image=image, gpu="L40S")
def inference(prompt):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="openai-community/gpt2", device=0)
    return pipe(prompt)

Key image methods:

• `.uv_pip_install()` — Install Python packages with uv (recommended)
• `.pip_install()` — Install with pip (fallback)
• `.apt_install()` — Install system packages
• `.run_commands()` — Run shell commands during build
• `.run_function()` — Run Python during build (e.g., download model weights)
• `.add_local_python_source()` — Add local modules
• `.env()` — Set environment variables

**Reference**: See `references/images.md` for Dockerfiles, micromamba, caching, GPU build steps.

GPU Compute

Request GPUs via the `gpu` parameter:

@app.function(gpu="H100")
def train_model():
    import torch
    device = torch.device("cuda")
    # GPU training code here

# Multiple GPUs
@app.function(gpu="H100:4")
def distributed_training():
    ...

# GPU fallback chain
@app.function(gpu=["H100", "A100-80GB", "A100-40GB"])
def flexible_inference():
    ...

Available GPU strings include T4, L4, A10, L40S, A100, A100-40GB, A100-80GB, RTX-PRO-6000, H100, H200, B200, B200+ and B300.

• GPUs are always specified as **strings** (e.g. `gpu="H100"`, `gpu="H100:4"`). The old `modal.gpu.*` objects are deprecated as of v0.73.31.
• Up to 8 GPU